Splitting the Electric Emissions Baby: Allocating Greenhouse Gas Reductions between Efficiency and Renewable Energy Policies

Energy Policy Institute’s Fifth Annual Energy Policy Research Conference

Scott Anders has 20 years’ experience working on energy and climate policy. He is Director of the Energy Policy Initiatives Center (EPIC), a research center of the University of San Diego School of Law. Mr. Anders joined EPIC in October 2005 as its inaugural director and developed both its academic and research programs. He has authored or co-authored numerous reports and papers on topics including energy efficiency, distributed generation, renewable energy, and policies to reduce greenhouse gases. Prior to joining EPIC, Mr. Anders directed policy and planning at the Center for Sustainable Energy and served in the Peace Corps in Mali, Africa. He holds an M.A. in Public Policy from the University of Maryland’s School of Public Policy. Jae D. Kim joined the Shiley-Marcos School of Engineering at the University of San Diego in 2014 as an Assistant Professor in Industrial and Systems Engineering. His research interests are in smart grid development, dynamic modeling of grid operations, renewable energy integration, and assessment of environmental impacts of new energy technologies. His current work focuses on modeling and operations of microgrids. Dr. Kim holds a Ph.D. and M.S. in Industrial & Systems Engineering from the University of Southern California. He also attended University of California Berkeley, where he received an M.S. and B.S. in Mechanical Engineering and a B.S. in Business Administration. Nilmini Silva-Send is the Assistant Director and the C. Hugh Friedman Fellow in Energy Law and Policy at the Energy Policy Initiatives Center (EPIC), a research center of the University of San Diego School of Law. In this role, she contributes research and analysis to EPIC’s ongoing energy and climate change projects. She is also a researcher in a multidisciplinary National Science Foundation Climate Science Education project. During her career Dr. Silva-Send has served in a variety of roles in which she conducted legal and technical analysis of environmental laws in Europe and the U.S., and conducted environmental due diligence assessments in Europe. She has taught upper level and graduate level international law courses, International and European Environmental Law. She holds a B.S. in Chemistry and a Ph.D. in International Law and Policy. Clark Gordon was until recently an energy policy analyst at the Energy Policy Initiatives Center (EPIC) at USD School of Law. His contributions to EPIC focused largely on energy and greenhouse gas emissions modeling. Prior to joining EPIC in May 2012, Mr. Gordon worked as an engineer for a research and development company focusing on renewable energy technology applications. His work focused primarily on hydro-mechanical solar energy collection systems. He holds a B.A. in Physics and Mathematics from Occidental College (Los Angeles) and a M.S. in Engineering Mechanics with a focus on Energy Dynamics from Columbia University. Mr. Gordon holds a J.D. from the University of San Diego School of Law. Yichao Gu is the technical policy analyst at USD’s Energy Policy Initiatives Center (EPIC), where her work focuses on developing greenhouse gas emissions inventories, assessing mitigation measures for local jurisdictions and the San Diego region, and further developing, maintaining and updating the tools and models to the best available data and methods. Prior to joining EPIC in July 2015, Ms. Gu worked as a research assistant at the California Department of Public Health, Indoor Air Quality Program. She received a Master in Science in Civil Engineering from University of California, Berkeley, with a focus on life-cycle assessment and the intersection of energy, civil infrastructure and climate science. She has a B.S. in Civil and Environmental Engineering, with an energy-environment focus, from the University of Illinois at Urbana-Champaign.

The work for this project was funded in part from a grant from The San Diego Foundation.

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Splitting the Electric Emissions Baby: Allocating Greenhouse Gas Reductions between Efficiency and Renewable Energy Policies When calculating the greenhouse gas impacts of policies in the electricity sector, an analytical challenge arises: how to allocate greenhouse gas emissions impacts between policies that lower consumption through efficiency and those that increase the supply of renewable electricity. This article demonstrates the challenge and proposes a solution to allocate emissions between the two policy types. Scott Anders, Jae D. Kim, Nilmini Silva-Send, Clark Gordon and Yichao Gu

I. Introduction California has been a leader in greenhouse gas reduction policies. The landmark Global Warming Solutions Act (AB 32) adopted in 2006 required that statewide emissions be reduced to 1990 levels by 2020. Executive Order S-3-05 adopted in 2005

sought to reduce emissions 80 percent by 2050.1 While much of the policy action has emanated from the state level, local governments also have become active participants in the state’s greenhouse gas reduction program. Climate action planning has become the main way local governments have engaged in the

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process. Major cities and counties in California such as Los Angeles and San Diego have adopted climate action plans, which typically include an inventory of emissions, a business-as-usual emissions projection, emissions targets, and mitigation measures to reduce emissions. uch guidance is provided for developing emissions inventories both for local government operations and the wider community,2 but less guidance is available for estimating emissions reductions from local actions.3 In California, there are several sources for such guidance but no standard protocols exist for greenhouse gas inventory development. There are several models available for estimating the emissions impacts of state and federal climate policies,4 but fewer publicly available comprehensive models for estimating the impacts of local and regional policies. Furthermore, there is no standard methodological protocol for estimating the portion of state and federal level policies that would accrue to local jurisdictions, an important part of the climate planning process.5 The need for protocols is particularly important, as many local agencies in California have been sued over various aspects of their climate action plans6 or greenhouse gas mitigation of broader regional plans.7 Recent and pending cases have heightened concern over such challenges. Most challenges to

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climate planning efforts have focused on legal issues relating to whether a plan is ‘‘enforceable’’ and whether estimated emissions reductions are adequate to reach stated targets. One case currently before the California Supreme Court challenged the methodology used to determine baseline emissions for a specific project and whether associated greenhouse gas emissions were considered significant under the California Environmental Quality Act.8 This trend highlights a potential need for further guidance on methods to estimate emission reductions for planning purposes. n important methodological consideration in estimating the emissions impacts of energy-related policies is to identify and account for the interactions between various measures in order to allocate emissions reductions accurately. Some guidance is available around the conceptual issue of the interaction challenge, but little specific methodological guidance exists,9 particularly for the type of scaled down models used at the local and regional levels. This article discusses the analytical challenge of allocating emissions between policies that affect the quantity (e.g., efficiency) of an emitting activity and those that affect the rate of emissions (e.g., renewable portfolio standard) of an emitting activity. In the process of calculating the net emissions impacts of an interconnected suite

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of policies, it is necessary to choose the order in which to calculate the impacts of some policies before the impacts of others; the ordering decision affects the magnitude of the emissions reductions for each policy. The article is not intended to be an exhaustive analysis of greenhouse gas emissions; rather, it focuses on the electricity sector to illustrate the effects of these methods. An illustrative model was used to demonstrate the methods described here. While the focus is on California with data specific to the San Diego region, the concepts are applicable and relevant to greenhouse gas estimates at any scope of analysis from a specific organization to national level policy analysis.

II. Overview of the Problem A commonly used method for measuring greenhouse gas emissions is to multiply the total level or quantity of a particular activity by a rate of emissions – or emissions factor10 – associated with the activity. For example, to estimate the emissions from electricity, it is necessary to multiply the total consumption (MWh) by the electric emissions factor (lbs CO2e/MWh). The same approach would be used to estimate the emissions associated with reducing electricity consumption. This relationship is The Electricity Journal

[(Figure_1)TD$IG] fundamental to most efforts to estimate greenhouse gas emissions across sectors including electricity, natural gas, transportation, water, and wastewater. However, though the relation may be simple and efficient for measuring total greenhouse gas emissions as in an inventory, it has limitations when used to estimate the emissions reductions11 associated with a particular policy or activity. ne challenge is the order in which the emissions reduction effects of policies are calculated. The fundamental calculation is to multiply the quantity of an activity by a rate of emissions for that activity. Most GHG mitigation policies related to energy fall into one of these two categories and this is generally true for measures related to electricity, natural gas, and transportation activities. Figure 1 illustrates the interrelationship of quantity- and rate-related policies. The sequencing of calculations determines the magnitude of the emissions reduction of each activity. For example, considering the renewable portfolio standard (RPS) first would lower the emissions factor and affect all subsequent emissions of other policies such as energy efficiency. That is, the net emissions effect of an energy efficiency measure would be calculated based on a lower emission rate since the RPS has already been accounted for prior to the efficiency measure. The result would overestimate the

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Figure 1: Relationship between Illustrative Quantity and Rate Measures (Note: An additional and related issue is the effect policies have on each other and between sectors. For example an increased RPS requirement would lower the rate of emissions per mile of the average passenger vehicles. Many cases of these relationships are a function of the emissions factor. One solution not discussed in detail here but assumed in certain calculations presented is to develop a dynamic emissions factor that changes based on the assumed level of policy implementation (e.g., percentage RPS for the electricity sector or the level of electric vehicles in the transportation sector). Also note that in our illustrative model, the electric emissions factor considers all electricity consumed (gross energy) not only that provided from the grid.)

emissions reduction from RPS and underestimate those from the energy efficiency measure. If the calculation order is reversed, then the opposite outcome arises: reductions from energy efficiency would be overestimated and those from the RPS would be underestimated. The total combined reduction for the two measures will be the same regardless of the order, but the amount allocated to each policy measures will be skewed by the order in which it was calculated. The difference between the values for the rate-related measures and the quantity measures can be significant and grows over time, depending on assumptions used. Using California’s new policy targets of 50 percent renewable electricity and a doubling of efficiency12 by 2030, the difference in the

emissions reduction values attributed to calculation order is significant. A. Illustrating the problem This section uses a simple hypothetical to illustrate the problem allocating greenhouse gas emissions reductions when certain mitigation measures affect the emissions factor (a rate) and other mitigation measures affect consumption (a quantity). When the various mitigation measures are considered sequentially, inaccurately favoring of either rate-related measures or quantity-related measures occurs. For the purposes of illustration, the following simple assumptions are used:  The business-as-usual electricity emissions factor before considering the effects of policies

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[(Figure_2)TD$IG] such as an RPS is 500 pounds of carbon-dioxide equivalent per megawatt-hour (lbs CO2e/MWh);  the RPS reduces the businessas-usual electricity emissions rate by 100 lbs CO2e/MWh;  the business-as-usual annual quantity of electricity consumption in a residential home is 6 MWh, and,  as a result of energy efficiency, annual electricity consumption is reduced by 1 MWh. Since this hypothetical involves both a GHG reduction measure affecting a rate (RPS) and a one affecting consumption (efficiency), an issue arises regarding how to allocate the emissions reductions. If the effects of both measures are determined sequentially, there are at least two possible calculation methods. The first method considers the effects of RPS before considering the effects of the efficiency retrofit (‘‘Rate-First method’’), and the second method considers the effects of RPS after considering the effects of the efficiency retrofit (‘‘Quantity-First method’’). Each method yields the same total reduction but different values for each measure depending on the method used. The focus here is how to allocate the total reduction between the two different types of measures. 1. Rate-First method (rate first, quantity second) In the Rate First method, the effects of RPS are considered first (independent of the effects of the 32

Figure 2: Allocation of GHG Emissions Reductions of RPS and Efficiency Measures Using Rate-First Method

efficiency retrofit). In this method, the emissions reductions due to RPS are calculated as a function of the business-as-usual annual electricity consumption. Using the previously stated assumptions, the emissions reductions due to RPS within the single residential home are 600 lbs CO2e (100 lbs CO2e/MWh  6 MWh) and the emissions reductions due to the efficiency retrofit are 400 lbs CO2e (400 lbs/MWh  1 MWh). Summing the emissions reductions for both mitigation measures results in total emissions reductions of 1,000 lbs CO2e. he Rate First method will overestimate the effects of RPS and underestimate the effects of the energy efficiency measure. Figure 2 graphically illustrates this outcome. The electricity emissions rate is on the vertical axis and the electricity consumption is on the horizontal axis. Using the Rate-First method, the dotted box shows reductions

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attributable to RPS, and the box with squares shows reductions attributable to the efficiency retrofit. 2. Quantity-First method (quantity first, rate second) In the Quantity-First method, the effects of the efficiency retrofit are considered first (independent from the effects of the RPS). The emissions reductions attributed to energy efficiency are determined by multiplying the business-asusual electricity emissions factor by the reduction in consumed electricity. This would result in emissions reductions of 500 lbs CO2e for energy efficiency (1 MWh reduction  500lbs/ MWh unmitigated electricity emissions rate) and 500 lbs CO2e for RPS (100 lbs CO2e/ MWh  5MWh of mitigated electricity consumption). Again, the sum of the emissions reductions for both measures equals 1,000 lbs CO2e. The Electricity Journal

[(Figure_3)TD$IG]

Figure 3: Allocation of GHG Emissions Reductions of RPS and Efficiency Measures Using Quantity-First Method

[(Figure_4)TD$IG]

Figure 4: Illustration of the Problem: Allocating the Overlapping Region

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he Quantity-First method yields an overestimate of the effects of the efficiency retrofit and underestimates the effects of the RPS. Figure 3 graphically shows this outcome. The dotted box shows reductions attributable to RPS, and the box with squares shows reductions attributable to the efficiency retrofit.

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3. Emissions allocation between the two methods The issue is that when the emissions reductions for the two types of mitigation measures are determined sequentially, the attributable GHG emissions reductions for each measure can differ significantly. The measure type calculated first will overestimate emissions reductions, and the

measure type calculated second will underestimate emissions reductions. Figure 4 highlights the issue graphically. If the effects of RPS are calculated first, then the emissions reductions defined by the overlapping region in the upper right box (diagonal lines) are attributed entirely to RPS. Conversely, if the effects of the efficiency retrofit are considered first, then the same emissions reductions defined by the overlapping region are entirely attributed to the efficiency retrofit. Table 1 summarizes the results from the Rate-First and QuantityFirst methods. Notice that both methods yield the same overall greenhouse gas emissions reduction. This would not be the case if the values from both methods were combined; that is, combining the overestimated value for the RPS in Method 1 and the overestimated value for efficiency in Method 2 would yield 1,100 lbs CO2e. Similarly combining the underestimates of each method would yield 900 lbs CO2e. he problem evolves from the original decision to measure total emissions reductions in terms of the product of a rate and a sum. Invariably, the two are dependent variables, and are inseparable. However, inaccuracy in the allocation of emissions reductions can be minimized. The following section proposes a method to distribute or ‘‘split’’ the quantity of overlapping emission reductions to the rate and quantity policies.

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Table 1: Comparison of Results from Both Methods. ‘‘Rate First’’ Method Emissions Reductions Due to RPS

600 lbs CO2e

Overestimate

Emissions Reductions Due to Efficiency Retrofit Total Emissions Reductions

400 lbs CO2e 1,000 lbs CO2e

Underestimate

‘‘Quantity First’’ Method Emissions Reductions Due to RPS

500 lbs CO2e

Underestimate

Emissions Reductions Due to Efficiency Retrofit

500 lbs CO2e

Overestimate

Total Emissions Reductions

1,000 lbs CO2e

III. Splitting the Electric Emissions Baby13 There is no standard protocol in the sequencing of calculation to prevent the allocation problem. Any assumption to put one measure before another is arbitrary. For example, California has a preferred order for serving incremental energy needs: efficiency first, then renewables, and then traditional sources.14 Using this order as a guide would overestimate the emissions from efficiency and underestimate the emissions from renewable energy. One way to overcome this problem is to allocate emissions between the quantity and rate policies based on the relative contribution to the combined reduction (rate plus quantity). To demonstrate the proposed method to split the overlapping emissions illustrated in Figure 4, this section provides the results for several scenarios, including California Gov. Jerry Brown’s policy goals, to demonstrate how estimated GHG reductions form renewable and energy efficiency can vary based on method. 34

A. Overview of proposed method The emissions reductions using the Rate-First method yield a maximum value for the RPS and a minimum value for efficiency. The opposite is true using the Quantity-First method. These two results provide a range of results (maximum minus minimum) for both policies. The range between the high and low values is the same in each case, which is represented by the overlapping region in Figure 4. Assuming that the maximum and minimum values in the range are inaccurate, a more representative allocation between rate and quantity measures lies somewhere in the middle. Therefore, the minimum values represent the ‘‘fixed’’ emissions reductions of each measure and the range value represents the ‘‘variable’’ emissions reductions of each measure. The proposed method – hereinafter ‘‘Splitting the Baby Method’’ – takes the following approach: allocate the variable emissions reductions based on a weighting factor that represents

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a measure’s contribution to the overall emissions reduction. For example, the weighting factor for the RPS measure would be the post-RPS measure adjusted emissions rate (i.e., lowered carbon intensity due to RPS) over the sum of the adjusted emissions rate and post-energy efficiency measure. A higher reduction in the emissions rate relative to the reduction due to the energy efficiency measure would yield a greater weighting factor for the RPS.15 An application of the method is shown in the next section. In the default method, this entails subtracting the allocated portion from the maximum value of the range.16 B. Splitting the Baby method applied to California’s energy policy targets California has aggressive statutory greenhouse gas reductions targets for 2020 and is considering codifying targets for 2030 and 2050. To support these overall emissions targets, California Gov. Jerry Brown announced policy targets for renewable energy and efficiency. In his 2015 inauguration speech, Governor Brown established a renewable electricity target of 50 percent by 2030 and an energy efficiency target to double building efficiency in this same timeframe.17 o further demonstrate the effects of the Rate-First and Quantity-First methods, and an application of the Splitting the

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Table 2: Three Scenarios to Demonstrate Allocation of GHG Emissionsa Scenario 2: Governor Brown’s 2030 Targets

Scenario 1: Business as Usual Energy Efficiency % Reduction From Projected Load

Utility Renewable Electricity % Renewable

Energy Efficiency % Reduction From Projected Load

Utility Renewable Electricity % Renewable

Scenario 3: Aggressive Energy Efficiency

Utility Renewable Electricity

% Reduction From Projected Load

% Renewable

2015

0%

22%

0%

21%

0%

21%

2020

3%

33%

6%

33%

6%

33%

2030 2050

9% 17%

33% 33%

17% 30%

50% 50%

17% 40%

50% 75%

a

All scenarios assume 1,000 MW of rooftop solar by 2020, 2,000 MW by 2030, and 3,000 MW by 2050. Similarly, all scenarios include 59 MW of shared solar by 2020, 100 MW by 2030, and 500 MW by 2050. Also note that the illustrative model uses a dynamic emissions factor that changes over time as policy targets change. The main effect of this is that as the electric emissions factor declines due to increased renewables, the emissions reductions from efficiency also declines. In an extreme example, if the electricity supply were 100 percent renewable energy by 2050, energy efficiency would yield no emissions reduction in that year. In fact, in that case, it would level off and bend to zero.

Baby method, we develop three scenarios: business-as-usual (Scenario 1), Gov. Brown’s 2030 Targets (Scenario 2), and Aggressive (Scenario 3). The business-as-usual scenario assumes that the current RPS requirement of 33 percent by 2020 is achieved and energy efficiency happens across all electricity at half the levels sought by Gov. Brown’s targets. Gov. Brown’s 2030 Targets scenario increases the RPS requirement to 50 percent by 2030 and doubles efficiency. Based on the California Energy Commission estimates, this scenario assumes that doubling efficiency in buildings equates to a 17 percent reduction in projected consumption levels. For simplicity, this efficiency level is applied to all electricity use. The final scenario takes the Gov. Brown scenario and increases RPS requirements to 75 percent by 2050 and continues energy efficiency at a slightly slower pace between 2030 and 2050. Table 2 October 2015,

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summarizes the assumptions used through 2050. 1. Demonstrating the allocation method The assumptions in Scenario 2 – Gov. Brown’s 2030 Targets are used to demonstrate the allocation method described above. Table 3 represents the values are for the San Diego region using Scenario 2 policy targets for 2030. A time series of the results from Scenario 2 are presented in Section 3.3. sing the above key variables, the range of emissions reductions for each policy type is 0.64 million metric tons carbon dioxide equivalent (MMT CO2e). In both cases the range is the same in absolute terms but different in percentage terms. The range provided here is the area of the overlapping box of emissions reductions in Figure 4. It is this value that should be divided and allocated to the rate and quantity policies. Table 4

U

summarizes the range of values for each calculation method. he proposed method to allocate the range uses an allocation factor based on the relative contribution of the policy to the overall reduction, which in this case is 4.5 MMT CO2e – the combination of the maximum and minimum reduction values from the Rate-First and Quantity-First methods. Using the values in Table 4 as an example, the total reduction would be the sum of 3.13 and 1.40 MMT CO2e or 3.78 and 0.76 MMT CO2e. In this illustrative example, the policy to increase renewable energy supply (rate) would contribute significantly more to the overall reduction than energy efficiency policy (quantity) and would therefore get a larger portion of the range value (0.64). Table 5 presents the allocation factors and the other key factors in determining the proposed allocation of the overlapping

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Table 3: Key Variables for Illustration of Proposed Allocation Method. Key Variable

Value

Baseline Electricity Emissions Factor (2015) Percent of Electricity Supplied by Renewable Resources (2030)

653 lbs CO2e/MWh 50%

Total Projected Consumption (2030)

27,866,818 MWh

17% Reduction below Projected Consumption due to Efficiency (2030)

4,737 MWh

C. Comparison of results for all scenarios

Table 4: Range of GHG Reduction Estimates Based on Each Method (MMT CO2e).

Rate Policies Quantity Policies

Minimum Value

Maximum Value

Range

Range as % of Minimum Value

3.13 0.76

3.78 1.40

0.64 0.64

20% 84%

Table 5: Factors in Determining Proposed Allocation (MMT CO2e). Maximum

Allocation

Amount Reduced

Proposed

Values

Range

Factor

from Maximum

Allocation

Rate Component

3.78

0.64

40%

0.25

3.52

Quantity Component

1.40

0.64

60%

0.39

1.01

emissions (range). In this example the product of the range and the allocation factor is subtracted from the maximum emission reduction value for each component.18

or Quantity-First method and the proposed allocation are relatively small for the rate component but fairly large for the quantity component.

Table 6 compares the GHG reductions from each method and the proposed allocation method. In this case, the difference between the Rate-First

The example provided in Section 3.2 only considers the 2030 results for Scenario 2. This section summarizes the results of extending the analysis through 2050. The following summarizes the results of applying the proposed allocation method to all three policy scenarios outlined in the previous section. Figure 5 summarizes the results for Scenario 1 – Businessas-Usual. The top cluster of lines shows the emissions reductions associated with rate related changes (RPS), in this case reaching 33 percent renewable electricity supply by 2020 and continuing at that level through 2050. The top line (black

Table 6: Comparison of Results Using Rate-First, Quantity-First, and Splitting the Baby Methods (MMT CO2e). Reduction Type Rate-First Method

Rate Component Quantity Component

Rate First Allocation

Proposed Allocation

3.78 0.76

3.52 1.01

% Change from Rate-First Allocation 7% 33%

% Change from Reduction Type Quantity-First Method

36

Quantity First Allocation

Proposed Allocation

Quantity-First Allocation

Rate Component

3.13

3.52

12%

Quantity Component

1.40

1.01

28%

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The Electricity Journal

[(Figure_5)TD$IG]

Figure 5: Results from Scenario 1: Business-as-Usual

diamonds) represents the emissions reductions when the rate effects are calculated first (Rate-First), the maximum value of the range, and the bottom line (black circles) is the reductions when the quantity effects are calculated first (Quantity-First), the minimum value. The dashed black line represents the emissions allocated using the approach presented above. imilarly, the bottom cluster of lines shows the emissions reductions associated with quantity related changes (efficiency), in this case reaching 8.5 percent reduction in electricity use by 2020 and 17 percent by 2050. The top line (gray triangles) represents the emissions reductions when the quantity effects are calculated first (Quantity-First), the maximum value of the range, and the bottom line (gray squares) shows the reductions when the rate effects are calculated first (Rate-First), the minimum value. The dashed gray line represents the emissions

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allocated using the approach presented above. Several observations can be made. First, the range between the two different calculation methods increases over time. The difference between the two approaches hardly matters in 2020 but becomes more pronounced by 2050 to about 0.5 MMT CO2e. For the rate measure, the upper value of the range is 20 percent higher than the lower value in 2050. For quantity measures, it is 43 percent higher. Such a range of results may be captured in the uncertainty analysis included in some analyses but may also be additional to that uncertainty.19 Second, the difference between the upper and lower lines for each approach is equal. This was also demonstrated in Table 4 for the Scenario 1 in 2030 example. Third, given the assumptions in this scenario, the split emissions (dashed line) are just below or above the middle of the range. For the rate measures it is just over the

middle of the range and the quantity measures is just under the middle. This is because the rate-related measures results in a higher contribution to the overall emissions reduction, therefore, increasing the overall reductions due to the rate-related measure. On the other hand, the quantity measure has a smaller contribution, therefore, decreasing the overall reductions due to the quantity-related measure. In Scenario 2, the range of emissions reductions between two approaches further widens to about 1.5 MMT CO2e by 2050 and the allocated value is higher in the range for the rate measure and lower in the range for the quantity measure (Figure 6). This follows the general concept of the proposed allocation method: the policy that contributes more to the combined reduction gets a higher allocation of the overlapping emissions reductions. The upper range value for rate measures is 43 percent higher than the minimum value in the range and for quantity measures, the difference is 96 percent. he trends between Scenario 1 and 2 continue in Scenario 3. The overall range by 2050 is over 3 MMT CO2e and the split value is closer to the maximum value for the rate-related values and closer to the minimum value for the quantity-related values (Figure 7). Figure 8 show the range between the maximum and minimum values for Rate-First

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[(Figure_6)TD$IG]

Figure 6: Results from Scenario 2: Governor Brown’s Policy Goals

[(Figure_7)TD$IG]

percent in 2050. The result is that the range grows over time and 100 percent of emissions reductions are allocated to the rate measure (Figure 9). As a consequence, no emissions reductions are attributed to efficiency in 2050 because the electricity that is offset has no associated emissions, though it likely would have a cost savings effect. In this case, the emissions reductions from quantity measures (gray squares) would peak around 2035 and taper to zero, as would the emissions using the proposed method (dashed gray). This example underscores the interconnected nature of many greenhouse gas reduction policies. missions reduction ‘‘wedges’’ are often used to demonstrate what combinations of policies could achieve mid- and long-term targets. These wedges are typically portrayed as ever expanding but as the supply gets more renewable, the quantity-based policies would decline as they would contribute less and less to overall GHG reductions. While the overall reduction is the same, it is important to understand the changing contribution of policies over time.

E

Figure 7: Results from Scenario 3: Aggressive

and Quantity-First methods for each scenario and how it was allocated using the Splitting the Baby method. In Scenario 1, the portion of the range allocated to the rate component is slightly higher than that of the quantity component. The allocation of the range is nearly equal between the rate and quantity components for all years in Scenario 1. This is because the contribution of each to the overall GHG reductions is relatively equal. In Scenario 2, the overall range is higher and 38

the contribution of the rate component increases, thus the allocation of the range to the rate component increases. In the aggressive scenario where 75 percent of the electricity supply is decarbonized, the portion of the range allocated to the rate component increases further. 1. Extreme scenario It is helpful to set inputs to extreme values to see if a model responds appropriately. To test this, the RPS was set to 100

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2. Mixing and matching results from Rate-First and Quantity-First methods The differing results illustrate the need for careful consideration in estimating and projecting emissions reductions from policies. If care is not taken, perverse outcomes could result (Figure 10). The Electricity Journal

[(Figure_8)TD$IG] values, and the actual values calculated, which are a combination of the Low and High values (Low-High, High-Low). The potential range by 2050 is about 3 MMT CO2e. It is not clear whether this could happen in actual GHG estimates but the possibility exists to overstate the savings in cases where this value is desirable. The opposite also could be true. Figure 8: Allocation of the Range to Rate and Quantity Components (Note: The height of the bar represents the total range to be allocated.)

[(Figure_9)TD$IG]

Figure 9: Results from Extreme Scenario: Aggressive Scenario with 100% Renewable Electricity

For example, if two high estimates are combined the total can be significantly higher than the actual value calculated, which is a combination of the Low and High values (Low-High, High-Low). Table 7 summarizes the possible combinations of values for the Gov. Brown’s Policy Goals Scenario. Selectively combining values could produce values significantly lower or higher than October 2015,

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the total reduction calculated using both the Rate-First and Quantity-First methods. As the range of values expands over time as demonstrates in the scenarios above, the artificially high and low numbers would differ significantly from the actual value by 2050. Figure 11 shows the range of the combination of High Rate and High Quantity values, those from Low Rate and Low Quantity

IV. Policy Implications The methods discussed here have implications across a wide range of policy and regulatory contexts, including scenario planning to explore combinations of policies to reach targets at any level of analysis (e.g., national, state, local, enterprise) and regulations for which both efficiency and renewable energy improvements are compliance options, such as the U.S. EPA Clean Power Plan. This issue is particularly important when estimating the emissions impact from particular policies. From the U.S. Environmental Protection Agency’s proposed Clean Power Plan to California estimating the impacts of the ambitious climate goals included Gov. Brown’s Executive Order B-30-1520 and 2015 Inaugural Address21 to cities developing climate action plans, analytical methods are needed to estimate the impact of policies to reduce greenhouse gas emissions.

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[(Figure_10)TD$IG] Table 7: Combinations of Methods (MMT CO2e). Quantity

Rate

Low

High

2020 Low

1.81

1.90

High

1.90

1.99

Low

3.90

4.54

High

4.54

5.18

4.50 5.53

5.53 6.56

2030 Rate

Figure 10: Possible Combinations of the Maximum and Minimum Values from Each Method

[(Figure_1)TD$IG]

2040 Rate

Rate

Low High 2050 Low

5.04

6.54

High

6.54

8.03

A

bsent accepted methods and protocols for calculating GHG reductions, there is room for error that policymakers and regulators should be aware of. This section briefly discusses examples where GHG reduction methods can have implications. A. Climate planning at the local and regional levels The climate planning process typically requires an emissions inventory for a baseline year. As mentioned above, accepted protocols exist to estimate emissions levels for a given year. Emissions reduction targets are based on the level of emissions in the baseline year and are typically expressed as a percentage reduction from the chosen baseline year. In general, 40

Figure 11: Comparison of Combined Values

the methods for accomplishing this part of the climate planning process are fairly well established. The next step is to project emissions into the future. Once a projection is established, a suite of mitigation measures is developed and associated greenhouse gas reductions are estimated to determine whether such actions would meet adopted targets. This is done through an analytical exercise that hinges in large part on the methods used. And as noted above, there are no accepted protocols for such calculations leaving potential room for error.

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B. California environmental quality act compliance The California Environmental Quality Act requires an environmental review of new construction projects to determine whether significant impacts would result. In 2007, California enacted SB 7, which added consideration of GHG emissions to the impacts analysis.22 The California Office of Planning and Research developed amendments to the CEQA Guidelines to incorporate GHG analysis and mitigation.23 Under these The Electricity Journal

Guidelines, new projects must assess whether expected greenhouse gas emissions would be deemed significant. This is typically done by determining baseline emissions without considering the effects of mitigation measures. Based on this analysis, if the impacts were deemed significant, the project would be required to incorporate greenhouse gas emissions mitigation measures to reduce emissions to a level consistent with overall statewide targets or those in a climate action plan. imilar to Climate Action Planning, demonstrating whether mitigation measures meet required emissions levels is typically done through an analytical exercise in which project proponents calculate the emissions impacts of measures and policies that would affect their project. The methods used to estimate the impacts of GHG mitigation measures effectively determine compliance.24 If mid- to long-run estimates for certain measures are based on methods that overstate reductions, projects could contribute to emissions at a level above and beyond those demonstrated in the GHG emissions analysis process, thus potentially increasing emissions beyond demonstrated levels.

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C. U.S. EPA clean power plan Policy implications also exist at the federal level. The proposed U.S. Environmental Protection October 2015,

Vol. 28, Issue 8

Agency Clean Power Plan allows states to use both renewable energy policies and energy efficiency to comply with emissions reduction targets.25 The Final Rule provided significant guidance about energy efficiency policies and evaluation to determine levels of energy reductions26 but no guidance on how to convert energy reductions into GHG emissions for purpose of compliance. Understanding the interconnections between quantity and rate related measures is important for regulators in evaluating compliance plans.

V. Conclusion Significant guidance is available to estimate GHG emissions but significantly less guidance is provided to estimate the emissions impacts from energy-related policies. The specific methodological issue presented here and a potential solution represents a discrete problem but underscores the importance of methodological consensus. As carbon dioxide increasingly becomes the currency of compliance for the electricity sector, it will be more important that common, accepted methods are used to demonstrate compliance, to minimize the level of error already inherent in GHG reduction estimates.& Endnotes: 1. Gov. Brown established a target of reducing statewide emissions to 40

percent below 1990 levels by 2030. See http://gov.ca.gov/news. php?id=18938. 2. For example, ICLEI has developed widely accepted and used protocols: Local Government Operations Protocol and United States Community Protocol for Accounting and Reporting of Greenhouse Gas Emissions. Available from: http:// icleiusa.org/tools/ghg-protocols/. The Governor’s Office of Planning and Research provided a Technical Advisory guidance document recommending that all local governments in California use these protocols for estimating and reporting greenhouse gas emissions. 3. A commonly used source for methods to estimate greenhouse gas reductions is CAPCOA Quantifying Greenhouse Gas Mitigation Measures A Resource for Local Government to Assess Emission Reductions from Greenhouse Gas Mitigation Measures August, 2010. Available from: http:// www.capcoa.org/wp-content/ uploads/2010/11/CAPCOAQuantification-Report-9-14-Final.pdf. 4. See Greenblatt, J., 2015. Modeling California policy impacts on greenhouse gas emissions. Energy Policy 78, 158–172. See also Williams, J., et al., 2012. The technology path to deep greenhouse gas emissions cuts by 2050: the pivotal role of electricity. Science 335 (January (6064)), 53–59. 5. One widely used model is ICLEI’s Clear Path. See http://californiaseec. org/software-tools. 6. Sierra Club v. County of San Diego. 7. CNFF v. SANDAG (California Supreme Court Case No. S223603). 8. CBD v. Dept. of Fish & Wildlife (California Supreme Court Case No. S217763). 9. See Rich, D., et al. Policy and Action Standard: An Accounting and Reporting Standard for Estimating the Greenhouse Gas Effects of Policies and Actions. World Resources Institute. Greenhouse Gas Protocol. Available from: http://ghgprotocol.org/ policy-and-action-standard. See also

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Boonekamp, P., 2006. Actual interaction effects between policy measures for energy efficiency: a qualitative matrix method and quantitative simulation results for households. Energy 31(14), 2848– 2873. Boonekamp, P., Faberi, S., 2012. Interaction between Policy Measures—Analysis Tool in the MURE Database. Report in the Frame of the Odyssee-MURE Project. Available from: www. odyssee-indicators.org. 10. In general, an emissions factor refers to a quantity of greenhouse gas emissions per unit of activity. For example, emissions factors for electricity are commonly presented with units of pounds or kilograms of carbon dioxide equivalent per megawatt-hour of electricity consumed (lbs CO2e/MWh). 11. Generally, emissions reductions refer to reductions in greenhouse gas emissions resulting from a greenhouse gas mitigation measure (e.g., a renewable portfolio standard, energy efficiency measures, etc.). 12. California Energy Commission defined this as a 17 percent reduction in building energy use by 2030. California Existing Building Energy Efficiency Action Plan. 2015. California Energy Commission. Available from: http://docketpublic. energy.ca.gov/PublicDocuments/ 15-IEPR-05/TN205919_ 20150828T153953_Existing_ Buildings_Energy_Efficiency_ Action_Plan.pdf. 13. ‘‘Splitting the baby’’ is a biblical reference to when Solomon suggested splitting a baby that was claimed by two mothers. It generally refers to application of wisdom to a tough problem and seems apt for considering how to allocate this overlapping box of emissions reductions in Figure 4. A more

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detailed treatment of the methodology described here, including mathematical proofs, is available in Gordon, C., Anders, S., 2014. Splitting the Electric Baby: A Methodology for Allocating Greenhouse Gas Emissions Reductions within the Electricity Sector (EPIC Working Paper). Energy Policy Initiatives Center, San Diego, CA. 14. May 2003 Energy Action Plan. The 2003 EAP was updated in the October 2005 Energy Action Plan II and in 2008. 15. See Gordon, C., Anders, S., (2014) for full description and demonstration of the method. 16. An alternative would be to use the inverse of the default allocation factor to determine how much of the rage would be added to the minimum. 17. Gov. Edmund G. Brown Jr. Inaugural Address, Jan. 5, 2015. Available from: http://gov.ca.gov/ news.php?id=18828. 18. Note the product of the range and the inverse of the weighting factor could be added to the minimum with the same result. 19. See Greenblatt, J., 2015. Modeling California policy impacts on greenhouse gas emissions. Energy Policy 78, 158–172. See also Williams, J., et al., 2012. The technology path to deep greenhouse gas emissions cuts by 2050: the pivotal role of electricity. Science 335 (January (6064)), 53–59. Available from: http:// www.sciencemag.org/content/335/ 6064/53.full. 20. Governor Brown established a target of reducing statewide emissions to 40 percent below 1990 levels by 2030. See http://gov.ca.gov/news. php?id=18938.

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21. Gov. Brown introduced three significant energy-related targets, including to increase renewable electricity supply to 50 percent, reduce current petroleum use by 50 percent, and double the efficiency of existing buildings by 2030. California Existing Building Energy Efficiency Action Plan. 2015. California Energy Commission. 22. See California Natural Resources Agency website. Available from: http://resources.ca.gov/ceqa/ guidelines/. See also California Office of Planning and Research website for more information about integrating GHG analysis into the CEQA process. Available from: http://www.opr.ca. gov/s_ceqaandclimatechange.php. 23. CEQA Guidelines § 15064.4 requires lead agencies to analyze the GHG impacts of a project to determine the significance of emissions. In cases where emissions are determined to be significant, CEQA Guidelines § 15126.4(c) requires that lead agencies consider a range of mitigation measures to reduce emissions. 24. Several tools exist to estimate GHG emissions from projects. See California Emissions Estimator ModelTM (CALEEMOD). Available from: http://www.caleemod.com/. See also URBEMIS. Available from: http://www.caleemod.com/. 25. 40 CFR Part 60. 26. U.S Environmental Protection Agency. 2015. Incorporating RE and Demand-Side EE Impacts into State Plan Demonstrations. Available from: http://epa.gov/airquality/cpp/ tsd-cpp-incorporating-re-ee.pdf. See also U.S Environmental Protection Agency. 2015. Demand-Side Energy Efficiency Technical Support Document. Available from: http:// epa.gov/airquality/cpp/ tsd-cpp-demand-side-ee.pdf.

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